Using cow data in the training population is attractive as a way to mitigate bias due to highly selected training bulls and to implement genomic selection for countries with no or limited proven bull data. However, one potential issue with cow data is a bias due to the preferential treatment. The objectives of this study were to (1) investigate the effect of including cow genotype and phenotype data into the training population on accuracy and bias of genomic predictions and (2) assess the effect of preferential treatment for different proportions of elite cows. First, a 4-pathway Holstein dairy cattle population was simulated for 2 traits with low (0.05) and moderate (0.3) heritability. Then different numbers of cows (0, 2,500, 5,000, 10,000, 15,000, or 20,000) were randomly selected and added to the training group composed of different numbers of top bulls (0, 2,500, 5,000, 10,000, or 15,000). Reliability levels of de-regressed estimated breeding values for training cows and bulls were 30 and 75% for traits with low heritability and were 60 and 90% for traits with moderate heritability, respectively. Preferential treatment was simulated by introducing upward bias equal to 35% of phenotypic variance to 5, 10, and 20% of elite bull dams in each scenario. Two different validation data sets were considered: (1) all animals in the last generation of both elite and commercial tiers (n = 42,000) and (2) only animals in the last generation of the elite tier (n = 12,000). Adding cow data into the training population led to an increase in accuracy (r) and decrease in bias of genomic predictions in all considered scenarios without preferential treatment. The gain in r was higher for the low heritable trait (from 0.004 to 0.166 r points) compared with the moderate heritable trait (from 0.004 to 0.116 r points). The gain in accuracy in scenarios with a lower number of training bulls was relatively higher (from 0.093 to 0.166 r points) than with a higher number of training bulls (from 0.004 to 0.09 r points). In this study, as expected, the bull-only reference population resulted in higher accuracy compared with the cow-only reference population of the same size. However, the cow reference population might be an option for countries with a small-scale progeny testing scheme or for minor breeds in large counties, and for traits measured only on a small fraction of the population. The inclusion of preferential treatment to 5 to 20% of the elite cows led to an adverse effect on both accuracy and bias of predictions. When preferential treatment was present, random selection of cows did not reduce the effect of preferential treatment.

在培训人群中使用奶牛数据是一种有吸引力的方法，它可以减轻因高度选择的培训公牛而造成的偏见，并为没有或仅有有限的经过验证的公牛数据的国家实施基因组选择。但是，奶牛数据的一个潜在问题是由于优惠待遇而产生的偏见。这项研究的目的是（1）研究将牛基因型和表型数据纳入训练人群对基因组预测的准确性和偏倚的影响，以及（2）评估针对不同比例的精英奶牛的优惠待遇的影响。首先，针对遗传性低（0.05）和中度（0.3）的2个性状模拟了4途径荷斯坦奶牛种群。然后是不同数量的母牛（0、2,500、5,000、10,000、15,000或20，000）是随机选择的，并添加到由不同数量的顶级公牛（0、2,500、5,000、10,000或15,000）组成的训练组中。遗传力低的性状对奶牛和公牛的回归估计育种值的可靠度分别为30％和75％，遗传力适中的性状分别为60％和90％。在每种情况下，通过将等于表型方差的35％的向上偏差引入5％，10％和20％的精英公坝来模拟优惠处理。考虑了两个不同的验证数据集：（1）上一代精英和商业级别的所有动物（n = 42,000）和（2）仅上一代精英级别的商业动物（n = 12,000）。在没有考虑优惠待遇的情况下，在所有考虑的情况下，将牛数据添加到训练种群中都会导致准确性（r）的提高和基因组预测偏差的减少。低遗传性状（从0.004至0.166 r点）的r增益高于中度遗传性状（从0.004至0.116 r点）。在训练公牛数量较少的情况下，准确性的提高相对较高（从0.093到0.166 r点），而在训练公牛数量较多（从0.004到0.09 r点）的情况下，准确度的提高相对较高。在这项研究中，正如预期的那样，仅公牛的参考种群比同等规模的牛的参考种群具有更高的准确性。但是，对于采用小规模后代测试计划的国家或大县中的小品种，奶牛参考种群可能是一种选择，对于仅在很小一部分人群中测得的特征。对5％至20％的精英奶牛包括优惠待遇对预测的准确性和偏倚都产生了不利影响。存在优惠待遇时，母牛的随机选择不会降低优惠待遇的效果。